# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for slim.inception_resnet_v2."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from nets import inception


class InceptionTest(tf.test.TestCase):

    def testBuildLogits(self):
        batch_size = 5
        height, width = 299, 299
        num_classes = 1000
        with self.test_session():
            inputs = tf.random_uniform((batch_size, height, width, 3))
            logits, _ = inception.inception_resnet_v2(inputs, num_classes)
            self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
            self.assertListEqual(logits.get_shape().as_list(),
                                 [batch_size, num_classes])

    def testBuildEndPoints(self):
        batch_size = 5
        height, width = 299, 299
        num_classes = 1000
        with self.test_session():
            inputs = tf.random_uniform((batch_size, height, width, 3))
            _, end_points = inception.inception_resnet_v2(inputs, num_classes)
            self.assertTrue('Logits' in end_points)
            logits = end_points['Logits']
            self.assertListEqual(logits.get_shape().as_list(),
                                 [batch_size, num_classes])
            self.assertTrue('AuxLogits' in end_points)
            aux_logits = end_points['AuxLogits']
            self.assertListEqual(aux_logits.get_shape().as_list(),
                                 [batch_size, num_classes])
            pre_pool = end_points['PrePool']
            self.assertListEqual(pre_pool.get_shape().as_list(),
                                 [batch_size, 8, 8, 1536])

    def testVariablesSetDevice(self):
        batch_size = 5
        height, width = 299, 299
        num_classes = 1000
        with self.test_session():
            inputs = tf.random_uniform((batch_size, height, width, 3))
            # Force all Variables to reside on the device.
            with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
                inception.inception_resnet_v2(inputs, num_classes)
            with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
                inception.inception_resnet_v2(inputs, num_classes)
            for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
                self.assertDeviceEqual(v.device, '/cpu:0')
            for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
                self.assertDeviceEqual(v.device, '/gpu:0')

    def testHalfSizeImages(self):
        batch_size = 5
        height, width = 150, 150
        num_classes = 1000
        with self.test_session():
            inputs = tf.random_uniform((batch_size, height, width, 3))
            logits, end_points = inception.inception_resnet_v2(inputs, num_classes)
            self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
            self.assertListEqual(logits.get_shape().as_list(),
                                 [batch_size, num_classes])
            pre_pool = end_points['PrePool']
            self.assertListEqual(pre_pool.get_shape().as_list(),
                                 [batch_size, 3, 3, 1536])

    def testUnknownBatchSize(self):
        batch_size = 1
        height, width = 299, 299
        num_classes = 1000
        with self.test_session() as sess:
            inputs = tf.placeholder(tf.float32, (None, height, width, 3))
            logits, _ = inception.inception_resnet_v2(inputs, num_classes)
            self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
            self.assertListEqual(logits.get_shape().as_list(),
                                 [None, num_classes])
            images = tf.random_uniform((batch_size, height, width, 3))
            sess.run(tf.global_variables_initializer())
            output = sess.run(logits, {inputs: images.eval()})
            self.assertEquals(output.shape, (batch_size, num_classes))

    def testEvaluation(self):
        batch_size = 2
        height, width = 299, 299
        num_classes = 1000
        with self.test_session() as sess:
            eval_inputs = tf.random_uniform((batch_size, height, width, 3))
            logits, _ = inception.inception_resnet_v2(eval_inputs,
                                                      num_classes,
                                                      is_training=False)
            predictions = tf.argmax(logits, 1)
            sess.run(tf.global_variables_initializer())
            output = sess.run(predictions)
            self.assertEquals(output.shape, (batch_size,))

    def testTrainEvalWithReuse(self):
        train_batch_size = 5
        eval_batch_size = 2
        height, width = 150, 150
        num_classes = 1000
        with self.test_session() as sess:
            train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
            inception.inception_resnet_v2(train_inputs, num_classes)
            eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
            logits, _ = inception.inception_resnet_v2(eval_inputs,
                                                      num_classes,
                                                      is_training=False,
                                                      reuse=True)
            predictions = tf.argmax(logits, 1)
            sess.run(tf.global_variables_initializer())
            output = sess.run(predictions)
            self.assertEquals(output.shape, (eval_batch_size,))


if __name__ == '__main__':
    tf.test.main()
